Optimal reactive power dispatch using self-adaptive real coded genetic algorithm

In this paper, self-adaptive real coded genetic algorithm (SARGA) is used as one of the techniques to solve optimal reactive power dispatch (ORPD) problem. The self-adaptation in real coded genetic algorithm (RGA) is introduced by applying the simulated binary crossover (SBX) operator. The binary tournament selection and polynomial mutation are also introduced in real coded genetic algorithm. The problem formulation involves continuous (generator voltages), discrete (transformer tap ratios) and binary (var sources) decision variables. The stochastic based SARGA approach can handle all types of decision variables and produce near optimal solutions. The IEEE 14- and 30-bus systems were used as test systems to demonstrate the applicability and efficiency of the proposed method. The performance of the proposed method is compared with evolutionary programming (EP) and previous approaches reported in the literature. The results show that SARGA solves the ORPD problem efficiently.     

Estimating the saddle-node bifurcation point of static power systems using the holomorphic embedding method

Voltage stability studies have been progressively gaining importance in the power engineering community. Predicting the saddle-node bifurcation point (SNBP) of a power system has become more critical as the power-system loading has increased in many places without a concomitant increase in transmission resources. Since a Newton–Raphson power-flow method is inherently unstable near the SNBP, adaptations such as continuation methods have been used as stabilizers. A new class of nonlinear equation solvers known as the holomorphic embedding method (HEM) is theoretically guaranteed to find the high-voltage solution to the power-flow problem, if one exists, up to the SNBP, provided sufficient precision is used and the conditions of Stahl’s theorem are satisfied by the equation set. In this paper, four different HEM-based methods to estimate the saddle-node bifurcation point of a power system, are proposed and compared in terms of accuracy as well as computational efficiency.     

Efficient real coded genetic algorithm to solve the non-convex hydrothermal scheduling problem

A simple and efficient optimisation procedure based on real coded genetic algorithm is proposed for the solution of short-term hydrothermal scheduling problem with continuous and non-smooth/non-convex cost function. The constraints like load-generation balance, unit generation limits, reservoir flow balance, reservoir physical limitations and reservoir coupling are also considered. The effectiveness of the proposed algorithm is demonstrated on a multichain-cascaded hydrothermal system that uses non-linear hydro generation function, includes water travel times between the linked reservoirs, and considers the valve point loading effect in thermal units. The proposed algorithm is equipped with an effective constraint-handling technique, which eliminates the need for penalty parameters. A simple strategy based on allowing infeasible solutions to remain in the population is used to maintain diversity. The same problem is also solved using binary coded genetic algorithm. The features of both algorithms are same except the crossover and mutation operators. In real coded genetic algorithm, simulated binary crossover and polynomial mutation are used against the single point crossover and bit-flipping mutation in binary coded genetic algorithm. The comparison of the two genetic algorithms reveals that real coded genetic algorithm is more efficient in terms of thermal cost minimisation for a short-term hydrothermal scheduling problem with continuous search space.     

Optimal power flow for a deregulated power system using adaptive real coded biogeography-based optimization

The optimization is an important role in the wide geographical distribution of electrical power market, finding the optimum solution for the operation and design of power systems has become a necessity with the increasing cost of raw materials, depleting energy resources and the ever growing demand for electrical energy. Using adaptive real coded biogeography-based optimization (ARCBBO), we present the optimization of various objective functions of an optimal power flow (OPF) problem in a power system. We aimed to determine the optimal settings of control variables for an OPF problem. The proposed approach was tested on a standard IEEE 30-bus system and an IEEE 57-bus system with different objective functions. Simulation results reveal that the proposed ARCBBO approach is effective, robust and more accurate than current methods of power flow optimization in literature.     

A new algorithm for analysis of SVC’s impact on bifurcations, chaos and voltage collapse in power systems

In this paper, a numerical algorithm, based on initial value problem, using local parameterisation continuation technique is proposed for tracing stable and unstable steady state periodic solution branches of power systems. Bifurcation diagrams of steady state solutions are constructed by the application of the proposed algorithm. From the bifurcation diagrams, the existence of various bifurcation points such as, unstable Hopf bifurcation (UHB), stable Hopf bifurcation (SHB), cyclic fold bifurcation (CFB), saddle node bifurcation (SNB) and period doubling bifurcation (PDB) are identified. With the use of tools of nonlinear dynamics, voltage collapse points, and chaotic solutions due to period doublings are unearthed. Simulations have been carried out to analyse the sensitivity of the system with respect to load reactive power and compensating capacitor. The impact of SVC on Hopf bifurcations and occurrence of SNB are investigated. The algorithm is validated by applying it to a standard power system reported in literature and the results obtained are presented.     

基于多种群遗传算法的波浪发电最大功率跟踪控制

波浪发电系统遗传算法最大功率点跟踪过程中,因群体中的所有个体较快趋于单一化而停止进化,导致难以获得最优解,为此引入多种群遗传优化新算法。在初始阶段,新算法引入多个种群同时进行搜索,并对每个种群赋予不同的交叉、变异概率,使算法能够兼顾全局与局部搜索;同时加入用于维持种群间联系的移民算子及可用来建立精华种群的人工选择算子,并以精华种群作为算法收敛的判据。仿真结果表明,与传统遗传算法相比,该算法能够提高波浪发电系统的波浪能捕获率。     

免疫遗传算法在多机电力系统PSS参数优化中的运用

电力系统稳定器参数的优化对于抑制多机电力系统低频振荡具有非常重要的意义。运用免疫遗传算法对多机系统PSS参数进行优化,将免疫操作融入基本的遗传算法中,运用免疫二次应答原理有效抑制了参数抗体在交叉、变异过程中的退化现象,提高了寻优过程的收敛性和稳定性。算例表明,采用免疫遗传算法对PSS参数优化其收敛性、计算时间和稳定性方面都优于传统遗传算法,将经过参数优化后的PSS加装到系统中,不反可以克服低频振荡现象,而且使系统的稳定性大幅度增强。     

基于Buck-Boost拓扑的新量子遗传算法 在MPPT技术中的应用

非线性的光伏电池要求在不同光照和温度等工况条件下实时变更电压、电流等负载特性实现MPPT追踪光伏电池最大功率点。针对一般遗传算法用于MPPT最大功率点追踪过程中出现的早熟、收敛慢等不足,提出一种改进的量子遗传算法与扰动法相结合的算法。在电路拓扑结构上采用Buck-Boost电路替代传统的Buck电路或Boost电路,并在Matlab/Simulink下进行了建模仿真。实验结果表明,该算法具有良好的最优点搜索能力和跟踪能力,且控制精度高,同时有效抑制了最大功率点附近的波动,证明了该控制方法的准确性和有效性。     

电力系统PMU安装地点选择优化算法的研究

将进化衰减因子引入了遗传算法,构造了一种新的自适应遗传算法。新算法在进化过程中能够同时根据个体适应度和进化时间的变化自动调整交叉与变异概率,克服了遗传算法易早熟的缺点,提高了最优解的多样性,加快了算法寻优速度。精英个体保留策略保证了整个算法的全局收敛性。算法约束条件处理采用了不可行解启发性修复方法,保证了全部优化结果都被严格限定在了满足约束条件的解空间内。基于图论的深度优先搜索方法用于系统可观性分析。将新的自适应遗传算法应用于优化PMU安装地点选择,实现了安装地点最少,而整个系统可观的目标。该算法已在某省     

基于改进免疫遗传算法的电力系统无功优化

针对无功优化问题的特点,在现有免疫遗传算法基础之上,提出一系列改进措施,形成了一种新的解决无功优化问题的改进免疫遗传算法。该算法将免疫遗传算法中常用的二进制编码改进为整、实数混合编码,提高了计算速度与精度;将通常的选择、变异操作与进化代数相联系,形成具有动态调整功能的改进Boltzmann退火选择、非均匀变异算子,提高了算法的全局收敛性,加快了计算速度;引入疫苗接种概念,有效地抑制了算法在进化过程中出现的退化现象,进一步加快了算法的收敛速度。以IEEE30节点系统为例对该改进算法的性能进行了测试,结果表明了该算法的有效性和可行性。     

遗传算法在变压器故障诊断中的应用

根据变压器油中溶解气体分析的三比值法建立了基于Mamdani模糊推理的变压器故障诊断系统。该故障诊断系统由历史数据库、模糊推理诊断模块和优化模块三部分组成。利用遗传算法和历史试验数据对模糊推理模块的隶属度函数进行优化．克服了传统的三比值法对变压器进行故障诊断时存在的临界值判据缺损问题,并可对故障诊断过程中的编码缺损情况作出解释。实例验证,该诊断系统具有较好的诊断能力,可提高变压器故障诊断的准确性。     

基于改进遗传算法的电力系统无功优化

遗传算法是一种模拟生物进化过程的优化算法,可用于求解包含离散化变量的复杂优化问题,该文将遗传算法应用于电力系统无功优化,并对常规遗传算法的编码方式、遗传算子以及终止判据等方面进行了改进,使用该文提出的算法对IEEE6、IEEE30节点系统进行了无功优化计算,结果表明该改进遗传算法应用于无功优化是合理可行的。     

基于改进遗传算法的无功综合优化

简要分析了传统的电力系统无功优化方法的局限性之后,提出了一种快速有效的求解方法——改进的遗传算法(IGA)。在简单遗传算法(SGA)的基础上,提出了自适应遗传算法,该算法采取了与个体分布散度成正比,并随最优个体保留代数成指数上升的自适应变异率;同时也采取了自适应的交叉率．该交叉率与群体中最大的适应度值和每代群体的平均适应度值有密切的关系。算例表明提出的算法优化效果好．而且在精度上和收敛速度上都有较大的提高。     

基于改进遗传算法的电力系统无功优化

将遗传算法应用于电力系统无功优化。针对传统遗传算法中存在的易陷入局部最优解和后期收敛速度慢的问题,在简单遗传算法（SGA）的基础上,提出更加有效的算法即改进遗传算法（IGA）。新算法结合灵敏度分析产生原始个体替代SGA。SGA 的交叉和变异被改进,改进的交叉操作拥有快速局部调节能力,改进的变异操作引入灵敏度分析产生新的个体。所提算法在一个算例上进行了分析验证。     

基于改进遗传算法的电力系统无功规划优化

简要分析了几种无功优化方法的局限性,通过比较得出遗传算法是求解无功优化的一种有效的方法,并在简单遗传算法(SGA)的基础上,提出了更加有效的算法即改进遗传算法(IGA)。该算法针对常规遗传算法收敛速度慢、易早熟等缺陷,并结合电力系统无功优化的特点,借鉴了模拟退火思想在遗传算法的执行过程中对个体适应度不断进行修正,并采用了浮点数编码、双层结构群体、自适应的交叉率和变异率等改进措施。算例表明这种改进的遗传算法优化效果好,而且在精度和收敛度上都有较大提高。     

一种求解最大输电能力的模糊算法

将模糊集理论和原始-对偶内点法应用于求解在最不利的负荷增长方式下并具有可伸缩不等式约束的最大输电能力问题。试验系统的计算表明,选用最不利的负荷增长方式,能更加准确地求得系统最大输电能力的下限值;将部分不等式约束模糊化,可求解出更加符合实际情况的最大输电能力。     

A new SPARLS algorithm for tuning power system stabilizer

Generators have to meet the change in real and reactive power demand of practical power system. The real power variations in the system are met out by the rescheduling process of the generators. But there is a huge trust to meet out the reactive power load demand. The excitation loop of the corresponding generator is adjusted with its electric limits to activate the reactive power of the network. To expedite the reactive power delivery, power system stabilizer (PSS) is connected in the exciter loop of the generator for various system conditions. In this paper, a new Sparse Recursive Least Square (SPARLS) algorithm is demonstrated to tune the power system stabilizer parameters to meet the vulnerable conditions. The proposed SPARLS algorithm makes use of expectation maximization (EM) updation to tune the PSS. A comparative study between the proposed SPARLS and RLS algorithm has been performed on single machine infinite bus system (SMIB). The simulation results obtained will validate the effectiveness of the proposed algorithm and the impact of stability studies of the power system operation under disturbances. The SPARLS algorithm is also used to tune the parameters of PSS to achieve quicker settling time for the system parameter such as load angle, field voltage and speed deviation. It is found that the SPARLS is a better algorithm for the determination of optimum stabilizer parameter.     

非解析复变电力系统动态无功点优化配置

动态无功补偿装置在电网中无功补偿的效果很大程度上取决于补偿点的选择。从非解析复变电力系统的动态分析方法出发,指出阻抗模裕度指标是电压稳定性分析最直观的指标,并利用该指标对系统薄弱环节进行一个初步判定。然后分析了系统在暂态扰动下失稳的过程,指出暂态稳定裕度指标是描述系统暂态扰动过程稳定性的最本质指标,利用动态无功补偿装置可以改善此指标。在此基础上,提出了一种结合阻抗模裕度指标和暂态稳定裕度指标的动态无功优化配置方法,比较各种无功配置方案下暂态稳定裕度指标的提升值以得到最优配置方案。通过IEEE39母线系统和广东电网220kV网络算例说明了该方法的有效性。     

Optimization of the electric power leveling system by using superconducting magnetic energy storage with genetic algorithm

A new optimization method of the electric power leveling system using an SMES is proposed. The SMES is parallelly connected with rolling mills in steel works. The leveling control is based on fuzzy reasoning. The SMES capacity and the scaling factors of the fuzzy controller will be optimized so that the power leveling control can be achieved and then the total cost of the added SMES cost and reduced contract electricity rate becomes lower. The optimization is carried out using a genetic algorithm and a cost reduction of 7.76 billion yen can be achieved. Power leveling simulation confirms that the proposed optimization method is very effective for designing the power leveling system. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 150(3): 62–69, 2005; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20049